Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models
arXiv:2606.05378v1 Announce Type: new Abstract: We test whether a single screen-and-ablate recipe -- identify attention-head circuits by task-pattern selectivity, then verify by causal ablation against a matched-random null -- produces consistent mechanistic claims across model families. The recipe ports across pipelines; the specific circuit it identifies does not. Across four composed tasks (indirect-object identification, greater-than, successor sequences, variable binding) and three 1B-class language models from distinct training pipelines (Pythia 1B / Pile / dense; OLMo 1B / DCLM / dense;
This research is emerging as the field of large language models matures, allowing for deeper mechanistic investigations into their internal workings.
Understanding how models process information at a circuit level is crucial for future AI development, enabling more reliable, interpretable, and efficient systems.
The findings suggest that current methods for identifying attention-head circuits may not generalize across different model architectures, indicating a need for more robust interpretability techniques.
- · AI interpretability researchers
- · Model developers striving for reliability
- · Organizations deploying critical AI systems
- · Overly simplistic mechanistic interpretability methods
Further research will be spurred to develop more universal and architecture-agnostic interpretability methods for large language models.
Improved understanding of model internals could lead to more efficient training paradigms and novel architectural designs.
Enhanced transparency in AI models may foster greater public trust and accelerate widespread adoption in sensitive applications.
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